Permanency and Safety Among Children in Foster Family and Kinship Care: A Scoping Review
Bibliographic record
Abstract
Over the past 25 years, kinship care placements have risen dramatically, such that when a child enters into care, child welfare agencies must first attempt to identify safe living arrangements with relatives or individuals known to the child before searching for alternatives. Despite the growing emphasis on kinship care, little is known about its impact on child outcomes in comparison to other placement types (e.g., foster family). Therefore, the aim of this scoping review was to evaluate quantitative research on children in out-of-home care from 2007 to 2014 with regard to the following outcomes: (1) permanency (i.e., reunification, reentry, placement stability, and adoption/guardianship) and (2) safety (e.g., additional reports to child welfare). Based on these objectives, the review identified 54 studies that examined permanency and safety among children in two major placement types, namely foster family and kinship care. Across studies, children in kinship care experienced greater permanency in terms of a lower rate of reentry, greater placement stability, and more guardianship placements in comparison to children living with foster families. Children in kinship care, however, had lower rates of adoption and reunification. The findings also indicated that differences in these variables diminish over time. Findings for safety outcomes were mixed. Study methodological limitations and recommendations for future research are considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".